During machine operations, the ability to detect or predict machine failure is essential in critical applications such as trucking, excavation, marine, construction, forestry and agricultural applications, and others or reduce machine downtime. To address such concerns various failure detection and diagnosis methods have been employed.
For example, many conventional approaches utilize the following three well known detection methods: knowledge-based detection, model-based detection, and signal-based detection, each of which applies a different detection approach. With knowledge-based detection, sensor readings are classified into time series and labeled to allow the information to be correlated for further detection. A drawback to such an approach includes the inability to precisely detect individual sensor failures, as well as decreased sensor bandwidth. Model-based detection, on the other hand, includes generating virtual sensors in the form of correlated “models,” which are compared with machine performance during machine operation. Similar to knowledge-based detection, drawbacks include decreased sensor bandwidths, as well as increased correlation processes. Further, although signal-based detection is a widely used detection method which focuses on frequency-domain analysis, it is also limited in its ability to accurately capture sensor failures.
As such, to overcome the limitations and drawbacks associated with the prior art, there is a need in the art for a new and improved detection method that provides increased sensor bandwidth, as well as more precise data analyses.